Critical failures across all dimensions. The single tool 'execute_skill_action' has no visible input schema, no parameter descriptions, and a trivial 35-character description. The MCP server implementation is non-functional: it only prints a JSON status message and exits; it does not implement the MCP protocol at all. No tool registration, no request/response handling, no schema validation. The codebase shows a client class and example usage but zero MCP server mechanics. This is a stub, not a working MCP server.

• Implement a proper MCP server request/response loop. Use the official MCP Python SDK (mcp package) or implement JSON-RPC 2.0 over STDIO with full request/response handling.
• Define the execute_skill_action tool with a complete JSON Schema input specification. Include 'model_weights_path' (string, required, file path format), 'target_quant_format' (enum: GGUF_Q4_K_M, GGUF_Q5_K_M, etc.), and any other parameters.
• Expand tool description to 100-200 characters. Example: 'Quantize a large language model to a lower bit-width format for edge deployment. Accepts a model weights file path and target quantization format. Returns compressed model size, compression ratio, and completion status.'
• Add descriptions to every parameter. Example for target_quant_format: 'Target quantization format. GGUF_Q4_K_M: 4-bit quantization (highest compression). GGUF_Q5_K_M: 5-bit (better quality). GGUF_Q8: 8-bit (minimal loss).'
• Document the output schema with typed fields: quantized_model_size_mb (number), compression_ratio (string), adapter_status (enum: QUANTIZATION_COMPLETED_EDGE_READY, QUANTIZATION_FAILED, etc.), and error_message (string, optional).